Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

Abstract

Identifying spot-like structures in large and noisy microscopy images is a crucial step to produce high quality results in various life-science applications. Imaging-based spatial transcriptomics (iST) methods, in particular, critically depend on the precise detection of millions of transcripts in images with low signal-to-noise ratio. Despite advances in computer vision that have revolutionized many biological imaging tasks, currently adopted spot detection techniques are mostly still based on classical signal processing methods that often lack robustness to changing imaging conditions and thus require tedious manual tuning per dataset. In this work, we introduce Spotiflow, a deep learning method that achieves subpixel-accurate localizations by formulating the spot detection task as a multi-scale heatmap and stereographic flow regression problem. Spotiflow can be used for 2D images and 3D volumetric stacks and can be trained to generalize across different imaging conditions, tissue types and chemical preparations, while being substantially more time- and memory-efficient than existing methods. We show the efficacy of Spotiflow via extensive quantitative experiments on a variety of diverse datasets and demonstrate that the enhanced accuracy of SO_SCPLOWPOTIFLOWC_SCPLOW leads to meaningful improvements in the biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.